Sentiment analysis is an ever-growing research topic, where the focus is to analyze the underlying sentiment of a given source of data, based on its subjectivity and context. Sentiment analysis is mostly performed using textual data, where the goal is to, based on a sentence or a text, determine the author's message polarity. The classification is generally binary - either negative or positive, or \(n\) -class classification, wherein, the most used one is a 3-class classification - negative, neutral and positive, using either machine-learning approaches, where a classifier is trained using a labelled corpus, or using lexicon-based approaches, where the textual information is classified based on its semantics or by using statistical approaches [1]}, [2]}. Applications of sentiment analysis can be seen in many contexts, such as brand awareness [3]}, political voting intentions [1]}, customer satisfaction [5]}, [6]} and in disaster relief [7]}.
